典型文献
Multi-Cluster Feature Selection Based on Isometric Mapping
文献摘要：
Dear editor,
This letter presents an unsupervised feature selection method based on machine learning. Feature selection is an important component of artificial intelligence, machine learning, which can effectively solve the curse of dimensionality problem. Since most of the labeled data is expensive to obtain, this paper focuses on the unsupervised feature selection method. The distance metric of traditional unsupervised feature selection algorithms is usually based on Euclidean distance, and it is maybe unreasonable to map high-dimensional data into low-dimensional space by using Euclidean distance. Inspired by this, this paper combines manifold learning to improve the multi-cluster unsupervised feature selection algorithm. By using geodesic distance, we propose a multi-cluster feature selection based on isometric mapping (MCFS-I) algorithm to perform unsupervised feature selection adaptively for multiple clusters. Experimental results show that the proposed method consistently improves the clustering performance compared to the existing competing methods.
文献关键词：
中图分类号：
作者姓名：
Yadi Wang;Zefeng Zhang;Yinghao Lin
作者机构：
Henan Key Laboratory of Big Data Analysis and Processing,Henan University,Kaifeng 475004;Institute of Data and Knowledge Engineering,School of Computer and Information Engineering,Henan University,Kaifeng 475004,China
文献出处：
引用格式：
[1]Yadi Wang;Zefeng Zhang;Yinghao Lin-.Multi-Cluster Feature Selection Based on Isometric Mapping)[J].自动化学报（英文版）,2022(03):570-572
A类：
Isometric
B类：
Multi,Cluster,Feature,Selection,Based,Mapping,Dear,editor,
This,letter,presents,unsupervised,feature,selection,machine,learning,important,component,artificial,intelligence,which,can,effectively,solve,curse,dimensionality,problem,Since,most,labeled,data,expensive,obtain,this,paper,focuses,distance,traditional,algorithms,usually,Euclidean,maybe,unreasonable,high,into,low,space,by,using,Inspired,combines,manifold,By,geodesic,we,isometric,mapping,MCFS,adaptively,multiple,clusters,Experimental,results,show,that,proposed,consistently,improves,clustering,performance,compared,existing,competing,methods
AB值：
0.558769
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